An Experimental Study of Acoustic Adaptation Algorithms

Abstract

Recently there has been much interest in the area of adaptation for improved speech recognition in the presence of mismatches between the training and testing conditions. In this paper we focus on transformation-based maximum-likelihood (ML) adaptation. Some of the important adaptation parameters include whether the adaptation is performed in the feature-space or model-space, and whether the adaptation is supervised or unsupervised. An additional parameter is the adaptation data. For example adaptation may be performed using an independent dataset or the test data itself. The latter is referred to as transcription-mode adaptation. In this paper, we experimentally study the eff ect of these various parameters, and report on our findings.